dgrAudit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).
Install via ClawdBot CLI:
clawdbot install sapenov/dgrPurpose: produce an auditable, machine‑validated decision record for review and storage.
Slug: dgr · Version: 1.0.4 · Modes: dgr_min / dgr_full / dgr_strict · Output: schema-valid JSON
DGR is a reasoning governance protocol that produces a machine‑validated, auditable artifact describing:
This skill is designed for high‑stakes or review‑required decisions where you want traceability and structured review.
dgr_min | dgr_full | dgr_strictThis skill does NOT guarantee:
DGR improves process quality (clarity, traceability, reviewability) — not outcome certainty.
Use when you need:
dgr_min, dgr_full, or dgr_strict.| Mode | Speed | Detail Level | Clarifications | Review Required | Use Case |
|------|-------|--------------|---------------|----------------|----------|
| dgr_min | Fastest | Minimal compliant output | Only critical gaps | Risk-based | Quick decisions, low stakes |
| dgr_full | Moderate | Fuller decomposition + alternatives | More proactive | Balanced | Standard decision support |
| dgr_strict | Slower | Conservative analysis | More questioning | Default on ambiguity | High-stakes, uncertain contexts |
A single JSON artifact matching schema.json.
Minimum acceptance criteria (see schema.json):
recommendation presentconsistency_check presentrecommendation.review_required = true.prompt.md — operational instructionsschema.json — output schema (stub aligned to DGR spec)examples/*.md — example inputs and outputsfield_guide.md — how to interpret DGR artifact fields1) Provide a decision request.
2) Choose a mode (dgr_min default).
3) The skill returns a JSON artifact suitable for review and storage.
1.0.4 — Remove redundant CLAWHUB_SUMMARY.md; summary now sourced from SKILL.md front-matter.
1.0.3 — Tighten front-matter description for better conversion, add reasoning category, compress identity block for faster scanning.
1.0.2 — Add ClawHub front-matter metadata with emoji and homepage for improved discovery and presentation.
1.0.0 — Initial public release of DGR skill bundle with auditable decision reasoning framework, governance protocols, and structured output format.
Note: This is an opt‑in reasoning mode. It is meant to be used alongside human decision‑making, not as a replacement.
Generated Mar 1, 2026
A bank uses DGR to assess loan applications, documenting assumptions about applicant credit history and risks of default. The JSON artifact provides an auditable trail for compliance reviews and internal audits, ensuring decisions are transparent and reviewable.
Medical professionals employ DGR to evaluate treatment options for patients, outlining assumptions based on medical history and risks of side effects. The structured output aids in multidisciplinary team reviews and regulatory documentation for high-stakes care decisions.
An IT team applies DGR during cybersecurity incidents to document assumptions about attack vectors and risks of system downtime. The artifact facilitates post-incident reviews and compliance with incident management protocols, enhancing traceability and accountability.
Law firms use DGR to formulate case strategies, detailing assumptions about evidence admissibility and risks of unfavorable rulings. The JSON record supports client consultations and audit trails for ethical and procedural oversight in legal proceedings.
Manufacturing companies leverage DGR to analyze supply chain disruptions, assuming supplier reliability and risks of production delays. The output enables structured decision-making and audit logs for operational reviews and stakeholder reporting.
Offer DGR as a cloud-based service with tiered pricing based on usage volume and mode features (e.g., dgr_min for basic, dgr_strict for premium). Revenue is generated through monthly or annual subscriptions, targeting enterprises needing auditable decision support.
Provide DGR as part of consulting packages for industries like finance or healthcare, where firms pay for customization, training, and integration into existing workflows. Revenue comes from project-based fees and ongoing support contracts.
License DGR's API to software developers and platforms, charging per API call or through enterprise licenses. Revenue is driven by usage-based pricing, enabling third-party applications to embed auditable reasoning into their tools.
💬 Integration Tip
Start with dgr_min mode for low-stakes decisions to familiarize teams with the JSON output, then scale to dgr_full or dgr_strict as needed for higher-risk scenarios, ensuring proper training on schema validation.
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